Locality condensation: a new dimensionality reduction method for image retrieval
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Image retrieval using nonlinear manifold embedding
Neurocomputing
Deep exploration for experiential image retrieval
MM '09 Proceedings of the 17th ACM international conference on Multimedia
To obtain orthogonal feature extraction using training data selection
Proceedings of the 18th ACM conference on Information and knowledge management
Fast Haar transform based feature extraction for face representation and recognition
IEEE Transactions on Information Forensics and Security
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
Discriminative orthogonal neighborhood-preserving projections for classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Constrained Laplacian Eigenmap for dimensionality reduction
Neurocomputing
Outlier-resisting graph embedding
Neurocomputing
Entropy controlled Laplacian regularization for least square regression
Signal Processing
A doubly weighted approach for appearance-based subspace learning methods
IEEE Transactions on Information Forensics and Security
Active reranking for web image search
IEEE Transactions on Image Processing
Document recommendation in social tagging services
Proceedings of the 19th international conference on World wide web
Evolutionary cross-domain discriminative hessian eigenmaps
IEEE Transactions on Image Processing
Laplacian regularized D-optimal design for active learning and its application to image retrieval
IEEE Transactions on Image Processing
Local histogram based geometric invariant image watermarking
Signal Processing
Semi-supervised local discriminant embedding
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Connected component in feature space to capture high level semantics in CBIR
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
Efficient manifold ranking for image retrieval
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Image retrieval algorithm based on enhanced relational graph
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Spectral Regression dimension reduction for multiple features facial image retrieval
International Journal of Biometrics
Discriminative information preservation for face recognition
Neurocomputing
Enhanced fisher discriminant criterion for image recognition
Pattern Recognition
Graph embedding based feature selection
Neurocomputing
Unsupervised face-name association via commute distance
Proceedings of the 20th ACM international conference on Multimedia
Efficient image and tag co-ranking: a bregman divergence optimization method
Proceedings of the 21st ACM international conference on Multimedia
Image retrieval based on augmented relational graph representation
Applied Intelligence
An improved distance-based relevance feedback strategy for image retrieval
Image and Vision Computing
Shape classification by manifold learning in multiple observation spaces
Information Sciences: an International Journal
Soft label based Linear Discriminant Analysis for image recognition and retrieval
Computer Vision and Image Understanding
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One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap been low level visual features and high level semantic concepts. To narrow down this gap, relevance feedback is introduced into image retrieval. With the user provided information, a classifier can be learned to discriminate between positive and negative examples. However, in real world applications, the number of user feedbacks is usually too small comparing to the dimensionality of the image space. Thus, a situation of overfitting may occur. In order to cope with the high dimensionality, we propose a novel supervised method for dimensionality reduction called Maximum Margin Projection (MMP). MMP aims to maximize the margin between positive and negative examples at each local neighborhood. Different from traditional dimensionality reduction algorithms such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) which effectively see only the global Euclidean structure, MMP is designed for discovering the local manifold structure. Therefore, MMP is likely to be more suitable for image retrieval where nearest neighbor search is usually involved. After projecting the images into a lower dimensional subspace, the relevant images get closer to the query image, thus the retrieval performance can be enhanced. The experimental results on a large image database demonstrates the effectiveness and efficiency of our proposed algorithm.